{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart\n",
    "> Fit an LSTM and NHITS model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an example on how to start using the main functionalities of the NeuralForecast library. The `NeuralForecast` class allows users to easily interact with `NeuralForecast.models` PyTorch models. In this example we will forecast AirPassengers data with a classic `LSTM` and the recent `NHITS` models. The full list of available models is available [here](../capabilities/01_overview).\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Getting_Started.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading AirPassengers Data\n",
    "\n",
    "The `core.NeuralForecast` class contains shared, `fit`, `predict` and other methods that take as inputs pandas DataFrames with columns `['unique_id', 'ds', 'y']`, where `unique_id` identifies individual time series from the dataset, `ds` is the date, and `y` is the target variable. \n",
    "\n",
    "In this example dataset consists of a set of a single series, but you can easily fit your model to larger datasets in long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast.utils import AirPassengersDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-01-31</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-02-28</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-03-31</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-04-30</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-05-31</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      y\n",
       "0        1.0 1949-01-31  112.0\n",
       "1        1.0 1949-02-28  118.0\n",
       "2        1.0 1949-03-31  132.0\n",
       "3        1.0 1949-04-30  129.0\n",
       "4        1.0 1949-05-31  121.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df = AirPassengersDF\n",
    "Y_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "DataFrames must include all `['unique_id', 'ds', 'y']` columns.\n",
    "Make sure `y` column does not have missing or non-numeric values. \n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `NeuralForecast.fit` method you can train a set of models to your dataset. You can define the forecasting `horizon` (12 in this example), and modify the hyperparameters of the model. For example, for the `LSTM` we changed the default hidden size for both encoder and decoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import LSTM, NHITS, RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "horizon = 12\n",
    "\n",
    "# Try different hyperparmeters to improve accuracy.\n",
    "models = [LSTM(input_size=2 * horizon,\n",
    "               h=horizon,                    # Forecast horizon\n",
    "               max_steps=500,                # Number of steps to train\n",
    "               scaler_type='standard',       # Type of scaler to normalize data\n",
    "               encoder_hidden_size=64,       # Defines the size of the hidden state of the LSTM\n",
    "               decoder_hidden_size=64,),     # Defines the number of hidden units of each layer of the MLP decoder\n",
    "          NHITS(h=horizon,                   # Forecast horizon\n",
    "                input_size=2 * horizon,      # Length of input sequence\n",
    "                max_steps=100,               # Number of steps to train\n",
    "                n_freq_downsample=[2, 1, 1]) # Downsampling factors for each stack output\n",
    "          ]\n",
    "nf = NeuralForecast(models=models, freq='ME')\n",
    "nf.fit(df=Y_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "The performance of Deep Learning models can be very sensitive to the choice of hyperparameters. Tuning the correct hyperparameters is an important step to obtain the best forecasts. The `Auto` version of these models, `AutoLSTM` and `AutoNHITS`, already perform hyperparameter selection automatically.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict using the fitted models\n",
    "\n",
    "Using the `NeuralForecast.predict` method you can obtain the `h` forecasts after the training data `Y_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_hat_df = nf.predict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `NeuralForecast.predict` method returns a DataFrame with the forecasts for each `unique_id`, `ds`, and model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>LSTM</th>\n",
       "      <th>NHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>445.602112</td>\n",
       "      <td>447.531281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>431.253510</td>\n",
       "      <td>439.081024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>456.301270</td>\n",
       "      <td>481.924194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>508.149750</td>\n",
       "      <td>501.501343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>524.903870</td>\n",
       "      <td>514.664551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds        LSTM       NHITS\n",
       "0        1.0 1961-01-31  445.602112  447.531281\n",
       "1        1.0 1961-02-28  431.253510  439.081024\n",
       "2        1.0 1961-03-31  456.301270  481.924194\n",
       "3        1.0 1961-04-30  508.149750  501.501343\n",
       "4        1.0 1961-05-31  524.903870  514.664551"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = Y_hat_df\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot Predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the forecasts of both models againts the real values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(Y_df, Y_hat_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "For this guide we are using a simple `LSTM` model. More recent models, such as `TSMixer`, `TFT` and `NHITS` achieve better accuracy than `LSTM` in most settings. The full list of available models is available [here](../capabilities/01_overview).\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "- [Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio (2020). \"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting\". International Conference on Learning Representations.](https://arxiv.org/abs/1905.10437)<br>\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
